VP of Infrastructure Scale

The core responsibilities for the job include the following:

Data Service Reliability and Platform Economics:

• Runs business-critical data services reliably and cost-effectively, around the clock.
• Operates under a 24x7 or follow-the-sun model with formal service-level objectives and disaster-recovery targets.
• Owns capacity planning and the cost per unit of work.
• Delivers measurable gains in availability, recovery time, data freshness, correctness, query performance, operational toil, or unit cost.

Multi-Discipline Platform Leadership:

• Leads across several platform disciplines and carries a long-term platform transformation through to measurable results.
• Leads through directors and senior managers across different technical disciplines.
• Carries a multi-year platform transformation through to completion.
• Shows measurable customer adoption, infrastructure efficiency, and business impact.

Database and Distributed-Systems Mastery:

Brings deep, hands-on expertise in running business-critical databases and distributed data systems at a large scale.

• Has built and run data platforms at very large production scales, such as billions of records.
• Has deep PostgreSQL knowledge, including concurrency control, locking, query planning, indexing, vacuum, replication, and recovery.
• Designs for consistency, partitioning, failure handling, and workload isolation.
• Diagnoses performance and correctness problems from first principles.

Modern Data Platform Leadership:

• Leads modern analytics and data platforms and separates analytical workloads from transactional systems.
• Has led platforms using a broad modern data stack, such as transformation frameworks, cloud data warehouses, streaming ingestion, distributed processing, data-quality tooling, and change data capture.
• Moves analytical workloads off transactional databases.
• Delivers measurable gains in scalability, performance, reliability, or cost.

Search, Integration, and AI Retrieval Platforms:

• Builds platforms that connect, index, and retrieve knowledge across structured and unstructured data.
• Combines graph modeling, indexing, distributed query execution, and retrieval.
• Delivers reusable integration frameworks that support many production enterprise integrations.
• Builds multi-tenant search with measurable targets for relevance, freshness, latency, access control, and infrastructure efficiency.